Geology & Geophysics

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1. 01Enquiry and requirement assessment
2. 02Technical scoping and proposal
3. 03Execution
4. 04Quality review and delivery, with an agreed round of revisions

Read the full project workflow.

In depth: how the work is done

1. Geophysical data processing workflows

   Processing sequences differ by method but share steps: quality control, noise removal, corrections, filtering and gridding. Seismic processing includes geometry, static corrections, deconvolution, velocity analysis, stacking and migration; potential-field processing includes reduction to the pole and derivative filters; electrical methods include contact resistance checks and data editing. We help record each step and its parameters, because reproducibility depends on knowing what was done to the data.

2. Forward modelling

   Forward models compute the response of a hypothetical earth model, letting researchers test whether a proposed structure can produce the observed data and what resolution a survey could achieve. Simple bodies provide intuition; numerical methods handle complex geometries. Comparing forward-model responses with data, including noise, shows which features are required by the data and which are optional.

3. Inversion concepts

   Inversion estimates model parameters from data using regularisation to stabilise ill-posed problems. The choice of regularisation, starting model and data weighting shapes the result. Depth of investigation and sensitivity maps indicate where the model is constrained by data. We advise testing different regularisation settings and reporting how the interpretation changes, and avoid interpreting features located where sensitivity is low.

4. Statistical analysis of geological data

   Orientation data, such as fault and fracture measurements, need directional statistics; grain-sise and thickness data often follow skewed distributions; stratigraphic data are ordered and autocorrelated. We choose methods matched to the data type, quantify uncertainty and test relationships, such as between porosity and velocity, with regression that respects measurement error and outliers.

5. Integration with geological constraints

   Boreholes, outcrops and structural interpretations constrain geophysical models and reduce ambiguity. Joint interpretation links different geophysical properties, for example density and velocity, through petrophysical relations that carry their own scatter. We document how constraints were applied and whether the final model remains consistent with each dataset.

6. Reporting geophysical interpretations

   A transparent report includes survey geometry, acquisition parameters, processing flow, inversion settings, data fit and sensitivity measures. Interpretations are labelled by confidence, and alternatives are shown. Figures use consistent scales and colour maps that do not distort perception.

7. Quality control of geophysical field data

   Good interpretation begins with the field data. Repeat measurements, base-station checks, contact-resistance logs and instrument calibration records show whether noise is within expectation. Positioning errors can matter as much as measurement noise, particularly for gravity and magnetic surveys. Outliers are inspected and documented, not deleted silently. Reporting these checks gives readers confidence that features in the final model are not artefacts of poor data.

8. Presenting uncertainty in sections and maps

   Geophysical sections look precise but are not. Overlaying sensitivity masks, showing multiple inversion results, or presenting ensembles of models communicates uncertainty. Colour scales should be fixed across comparable figures and chosen so that small differences are not exaggerated. Captions state the inversion settings and data fit, so each figure can be read with its limitations in view.

Common pitfalls this service helps avoid

• Interpreting artefacts of processing. Filters and migration choices can create or remove apparent structure.
• Trusting inversion where sensitivity is low. Features outside the depth of investigation are not constrained by data.
• Ignoring non-uniqueness. Different models can give the same data fit.
• Applying a petrophysical relation outside its range. Empirical relations have limited validity.

What good reporting looks like

A geophysics report states the survey and acquisition, the processing sequence and parameters, the modelling and inversion set-up, the data misfit, sensitivity and resolution analysis, the geological constraints, the interpretation with alternatives and the limitations. It is research support and not a substitute for the professional interpretation required for resource, engineering or safety decisions.

An illustrative scenario

Hypothetical example written for explanation. It is not a client case, a result or a testimonial.

Suppose a researcher interprets a resistivity section as showing a conductive layer at 20 m depth. A sensitivity analysis shows poor resolution below 15 m, and an inversion with different regularisation moves the layer boundary by several metres. A borehole log supports a conductive clay layer between 18 and 24 m. The report presents the section with the sensitivity mask and states that the thickness of the layer is uncertain.

Questions to settle before you begin

• Which processing choices could create my main feature?
• Where is my model constrained by data?
• How do results change with regularisation?
• What independent geological data constrain the interpretation?
• Which alternative models should I present?

Key terms

Regularisation

Constraints added to stabilise an ill-posed inversion.

Migration

Processing that repositions reflections to their true subsurface locations.

Depth of investigation

The depth below which data no longer constrain the model.

Reduction to the pole

A magnetic data transformation that simplifies interpretation.

Petrophysics

The study of physical properties of rocks.

Working together on Earth Sciences & Geosciences projects

Earth sciences engagements start with the dataset or field question: location, scale, sampling approach, measurement methods and the uncertainties in each. We ask for data tables, maps or logs in standard formats, the conceptual model you hold and any prior interpretations. Analyses make explicit how spatial and temporal sampling limits what can be concluded. Reports describe uncertainty and non-uniqueness, as in geophysical inversion, and separate observation from interpretation. Site-specific engineering or hazard decisions require qualified professionals and are outside the scope.

Few projects need a single service in isolation. These are the services most often combined with this one.

• Earth Sciences Research

  Research support for geological, geophysical and environmental earth science studies, combining spatial data analysis, statistics and modelling with explicit uncertainty.

• Geotechnical Engineering

  Analysis of soil and rock behaviour for foundations, slopes, retaining structures and ground improvement, using analytical methods and numerical modelling with documented parameter selection.

• Petroleum Engineering

  Research support on reservoir, drilling and production engineering: reservoir-simulation concepts, production-data analysis and data-driven modelling.

• Seismology & Natural Hazard Modelling

  Analysis of seismic data and modelling of natural hazards such as earthquakes, landslides and floods, with probabilistic thinking and clear communication of uncertainty.